Presentation
RBF-Driven Analog Neural Networks for Flexible Wearable Near-Sensor at the Extreme Edge
DescriptionFlexible Electronics (FE) enable lightweight, conformable, and low-cost wearable devices. Still, their limited integration density and device dimensions impose tight area and power constraints, making on-substrate neural network classifiers challenging. A fully digital realization requires full-bandwidth sensor readout and power-hungry ADCs, whereas analog classifiers can operate directly on continuous-time signals on the flexible substrate and use only a simple comparator at the output. Wearable sensing pipelines must tolerate sensor noise, motion artifacts, and baseline drift, which places strong robustness demands on low-precision hardware. Existing analog neural networks in large-area electronics use smooth, monotonic activation functions that offer limited resilience to such disturbances. Radial Basis Function (RBF) kernels, by contrast, provide bounded, local similarity responses naturally compatible with current-mode analog implementations and help suppress signal excursions. We propose RBF-based activation kernels for analog neural networks in FE and demonstrate, via system-level experiments, improved robustness over standard activations at comparable circuit complexity.
Event Type
Research Special Session
TimeTuesday, July 285:00pm - 5:30pm PDT
LocationMtg Room 201A
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